Background: Quality measurement needs to be embedded within EHR systems and become much more,[unreadable] dynamic, accurate and detailed in order to provide the highest level of care possible to all patients. Over the[unreadable] last two years, we have developed quality measurement programs to use our EHR data to measure quality of[unreadable] care for coronary artery disease, heart failure, diabetes, hypertension, and preventive services, and this data is[unreadable] now used to provide physicians with individual and group-level quarterly quality reports. We are now poised to[unreadable] take the next step and create systems that improve our quality data and seamlessly link this data to practicelevel[unreadable] quality improvement programs and point of care interventions. Specific Aims: Aim 1- Integrate simple,[unreadable] standard ways for clinicians to document patient reasons or medical reasons for why quality measures are not[unreadable] met and assess the use of these exception codes, the impact of exception reporting on measured levels of[unreadable] quality, and the impact of using these codes on physician satisfaction and self-reported efficiency; Aim 2- Use[unreadable] the exception codes (patient reasons and medical reasons) that clinicians enter to target three forms of quality[unreadable] improvement, including a) peer review of all medical reasons for not adhering to guidelines followed by academic[unreadable] detailing if a clinician enters an unjustified reason for not following guidelines, b) counseling for patients[unreadable] whose physician enters an exclusion code stating that the patient cannot afford a needed medication to[unreadable] determine ways of overcoming barriers; and c) educational outreach to all patients who refuse recommended[unreadable] interventions (e.g., colorectal cancer screening), including mailing of plain language health education materials[unreadable] or DVDs); Aim 3- provide clinicians with highly accurate information on patients? quality deficits immediately[unreadable] prior to their visit as part of routine work flow and assess whether this intervention increases provision of[unreadable] recommended therapies/tests, and documentation of exclusion codes. Methods: This study will begin at a[unreadable] large academic internal medicine practice and then be implemented in 3 community practices that use the[unreadable] same electronic health record (EHR), Epic(R). Exception codes will be introduced into the EHR for 17 national[unreadable] quality measures. Data will be extracted from the EHR every 2 months to assess changes in the primary[unreadable] outcome, the proportion of eligible patients who do not satisfy a measure and do not have an exclusion criteria[unreadable] documented. The statistical significance of changes will be assessed with hierarchical, longitudinal modeling.[unreadable] In addition, physicians will be surveyed multiple times to assess their attitudes towards the interventions[unreadable] described in the Aims, and the outcomes of the quality improvement activities will be monitored along with the[unreadable] costs of the intervention. Dissemination: This study will produce computerized tools and educational[unreadable] materials that will allow rapid dissemination to over 1000 sites that use the Epic(R) ambulatory product.